Tianyi Wei supports RSG’s travel model development efforts with expertise in transportation planning, travel forecasting, and data science. She combines advanced analytical methods with programming and visualization tools to develop, calibrate, and enhance transportation models that support planning and investment decisions.
At RSG, Tianyi applies her experience in tour-based and activity-based travel demand modeling, model validation, and workflow automation to improve model performance and usability. She has developed Python-based tools that streamline data processing, reporting, and model integration, and has experience with a wide range of transportation modeling platforms, including PTV Visum, Bentley CUBE, STOPS, and GIS applications. Her work reflects a strong commitment to creating efficient, transparent, and technically rigorous modeling solutions.
Tianyi is actively involved in the transportation profession through leadership roles with the Women’s Transportation Seminar (WTS) and Young Professionals in Transportation (YPT). Outside of work, Tianyi can often be found rock climbing, hiking a new trail, or planning her next adventure.
Education
- MS, Transportation Engineering, University of California, Berkeley
- Graduate Certificate, Applied Data Science, University of California, Berkeley
- BE, Traffic Engineering, Tongji University
